Sir! I’d Rather Go to School, Sir!∗
Mahdi Majbouri†
Babson College
Abstract
Would the fear of conscription entice young men to get more education against
their will? This paper uses a discontinuity in the military service law in Iran to answer
this question. Iranian males become eligible for military service when they reach 18.
But, between 2000 and 2010, sole sons whose fathers’ age was over 58, at the time of
son’s eligibility, were exempted from the service. Sole sons whose fathers’ age is a bit
below the threshold may stay in school until their father reaches (or passes) 59, in order
to get exemption after leaving school. This study shows that, as a result, there is a
discontinuity in education levels of sole sons at the father’s age of 59. Sole sons whose
fathers’ age was below the threshold are 13 percentage points (20 percents) more likely
to attend college than those whose fathers’ age was above it. This exogenous increase
is used to estimate returns to college education in Iran.
JEL Code: J31, J47
Keywords: Conscription, Coercive labor market, Natural experiment, Regression dis-
continuity, Higher-educational attainment
∗For their comments and suggestions on an earlier version of this paper, the author wishes to thankKristin Butcher, Patrick J. Ewan, Pinar Keskin-Bonatti, and other attendants of the Wellesley CollegeEconomics department brown-bag lunch series, as well as Erica Field, Jacob Shapiro, Thomas Pepinsky,and the participants of the AALIMS 2015 conference at Princeton University, and the UNU-WIDER 2015conference on Human Capital and Growth, at Helsinki. Sincere thanks go to Sigrid Sommerfeldt for reviewingthis manuscript. All the remaining errors are mine.†Correspondence to: Mahdi Majbouri, Babson College, Economics Division, 231 Forest St., Wellesley,
MA 02457, USA; [email protected]; phone: 781-239-5549
1
1 Introduction
96 out of 99 countries that experienced internal armed conflict between 1946 and 2014 are
developing countries.1 Moreover, at least one side of all 122 interstate armed conflicts in the
same period has been a developing country.2 This indicates that in developing countries,
having a strong yet relatively inexpensive army is critical for the state. Military service,
which is mandatory in over 60 countries around the world (Chartsbin, 2011), is a relatively
inexpensive way governments use to recruit for their armed forces. Yet, its compulsory
nature remains controversial.
Despite the prevalence and importance of conscription, its different aspects and con-
sequences, particularly in developing countries and at the time of peace, have remained
under-researched.3 As one of the few studies on this topic, this paper uses a novel natural
experimental setting to document whether and how much (if any) the compulsory nature
of military service is abhorred in a developing country, at the time of peace. The results
draw on some of the fundamental pillars of economic science, incentives, decision making,
and inefficiency and use data from one of the most understudied developing countries, Iran.
Military service for males 18 and above is compulsory in Iran. But in some cases exemp-
tion is possible. For instance, certain disabilities and chronic conditions make one eligible for
permanent exemption. College and graduate students are also exempted (temporarily) from
the service during their studies. But, one particular case of exemption is that of a sole son.
Between 2000 and 2011, a sole son could get exemption to take care of his elderly father,
if his father was 59 or older (over 58) when he had reached his 18th birthday and became
1The three developed countries are France, Spain and United Kingdom. Source is author’s calculationfrom UCDP/PRIO Armed Conflict Dataset, Version 4-2015. The dataset is provided by Uppsala ConflictData Program (UCDP) and Center for the Study of Civil Wars, International Peace Research Institute,Oslo (PRIO). See Gleditsch et al. (2002), Pettersson and Wallensteen (2015), and Themner (2015) for thedescription of the dataset. In this dataset, an armed conflict (internal or interstate) is defined as “a contestedincompatibility that concerns government and/or territory where the use of armed force between two parties,of which at least one is the government of a state, results in at least 25 battle-related deaths.” For a morein-depth discussion on definitions, see http://www.pcr.uu.se/research/ucdp/definitions/.
2Source is author’s calculation from UCDP/PRIO Armed Conflict Dataset, Version 4-2015. For a defini-tion of conflict, as well as information on data, see footnote 1.
3As will be discussed, most of the literature is about developed countries and draft at the time of war.
2
eligible for the military service.4 This means those sole sons whose fathers are younger may
miss this opportunity. But they have a way to benefit from this exemption as well. An 18
year-old sole son whose father’s age is below but close to 59, can get temporary exemption
(like everyone else) by going to school (i.e. college) for a few years, and postpone his con-
scription. In the meantime, his father will reach (or pass) the age of 59, as a result of which
he can get exemption. Therefore, those 18 year-old sole sons whose fathers’ age is above 58
(59 or older) have less incentive to continue their education than those whose father is 58 or
younger.
This study establishes that, in Iran, there is a discontinuity in college attainment rate of
sole sons, whose fathers’ age was over 58 when they were 18 years old, relative to the sole sons
with slightly younger fathers. The size of this discontinuity is at least about 13 percentage
points (20 percent). As a robustness check, this study shows that there is no discontinuity in
college attainment among the sisters of sole sons, or the sons and daughters of multiple-son
households. Since the military exemption law should not affect any of these groups except
the sole sons, the results provide strong evidence that military exemption law created such
discontinuity. In other words, fear of conscription entices sole sons to gain more education
against their will. If there had not been such an exemption law, sole sons of younger fathers
would not have attended college. This exogenous increase in college attendance may be used
to estimate returns of college on various outcomes, such as wages. But, because of data
limitations this exogenous increase turns out to be a weak instrument and the IV estimates
are not reliable.
By showing that individuals are willing to get education despite their will to avoid con-
scription, this study provides evidence for the inefficiency of conscription. But, moreover, it
offers a lower bound (minimum) for the size of these inefficiencies.
There have been many studies on the impact of conscription in developed countries, and
4The required age of father has increased to 65 in 2012 and then to 70 in 2014. Later, the age requirementwas abrogated and replaced with a father’s disability that impedes him from working. This has remainedthe law until now that this paper is written.
3
almost all during the war time. Angrist (1990) is a seminal work that uses Vietnam draft
lottery as a natural experiment since draft-eligible individuals were conscripted based on
randomly chosen birth-days. Most studies that come later (and all cited here) use the same
natural experiment. Angrist (1990) studies the effect of conscription at war time on earnings
of US veterans later in life. He finds 15% lower earnings for veterans vs. non-veterans
in 1970s and 80s. Angrist et al. (2011), however, revisit the question 20 years later and
surprisingly find that the gap between veterans and non-veterans earnings disappeared by
the early 1990s. Angrist and Chen (2011) attribute this to flattening of wages in mid-life and
increase in schooling of veterans due to the Vietnam War GI Bill. Autor et al. (2011), on the
other hand, find a decline in employment and a rise in disability receipts by Vietnam War
Veterans (particularly for Post-Traumatic Stress Disorder) in 2000s; this is while Angrist
et al. (2010) found no evidence of either one in 1990s. Conley and Heerwig (2012) find that
interestingly, there is no impact of draft on post war mortality of veterans vs. non-veterans
in the long-run. Siminski and Ville (2011) find the same results for Australian veterans who
were drafted during the Vietnam War. All studies on various impacts of the Vietnam-era
draft (and there are many5), inevitably use this draft which was during a war. Therefore, it
is hard to disentangle the effect of war vs. the effect of conscription in these studies. They
remain silent about the effect of conscription during peacetime.
There is very little research on the impacts of conscription in peacetime on various out-
comes and markets, such as human capital accumulation (cognitive and non-cognitive skills,
educational attainment, and health), earnings, hours worked, labor markets, and marriage
markets. Two studies that stand out are Bauer et al. (2012) and Card and Cardoso (2012).
Using regression discontinuity, Bauer et al. (2012) compare cohorts who were born before
July 1, 1937 in Germany and hence exempted from service in 1950s with those who were
5For example, Schmitz and Conley (2016) find that “veterans with a high genetic predisposition forsmoking were more likely to have been smokers, smoke heavily, and are at a higher risk of being diagnosedwith cancer or hypertension at older ages.” They also find that the effect is smaller among those who went tocollege after the war. Conley and Heerwig (2011) study the impact of the same draft on residential stability,housing tenure, and extended family residence after the War and find mixed results.
4
born right after (who had to serve in 1950s). They find no effect of conscription on wages
later in life. Interestingly, in a different design, Card and Cardoso (2012) also find no effect
of conscription on Portuguese with more than primary education, although they find positive
impact on less educated individuals.
These studies are about developed countries. Nevertheless, if there is no effect on earnings
(in those countries) or a positive effect in the case of less educated Portuguese men, one
wonders how much people are willing to avoid it. This study endeavors to answer this
question. Card and Lemieux (2001) answer a similar question: whether people were willing
to go to college to avoid draft during the Vietnam War. But, they again look at a draft
during a war, while this paper is about draft during peacetime. It is not very surprising to
see people were willing to go to college to avoid a war. But, it is interesting to know whether
people do that in peacetime, in a developing country where supply of college education is
limited and entering college is very competitive and costly. Moreover, as Card and Lemieux
(2001) explain, they “find a strong correlation between the risk of induction faced by a cohort
and the relative enrollment and completed education of men.” This study, however, finds a
causation.
The rest of the paper is structured as follows. Section 2 explains the military service
and its exemption laws in Iran. It also explains the identification strategy. The data are
discussed in Section 3 after which the estimation and results are depicted in Section 4. The
conclusion discusses policy implications for countries with military service laws.
2 Military Service in Iran
Conscription, in its modern form, was first introduced by the French Republic in the wars
after the French Revolution6 to protect the country from attacks by other European powers.
Later, however, it made the French army one of the most powerful military in the early
6Britannica Academic, s.v. “Conscription,” accessed August 13, 2016,http://academic.eb.com.ezproxy.babson.edu/levels/collegiate/article/25932#.
5
1800s. With the rise of nationalism in Europe and the rest of the world in the 19th century,
this system became popular with the governments around the world to create and maintain
an army.7 In recent decades, however, criticism from various angles (religious, philosophical,
economic8, political, and human rights grounds9) made conscription controversial. Moreover,
after the end of the cold war and with the advances in military technology, which reduced the
need for large armies10, many states abandoned this system and began to rely on professional
armies and volunteers.11
In Iran, mandatory military service for men was first introduced in 1924 by Reza Shah,
the king at the time, to the Parliament and, despite some opposition, became the law.12
After the Islamic Revolution of 1979, this law was modified in 1984 (in the middle of Iran-
Iraq War), and stated that conscription consists of 30 years and is divided into four periods:
First, a two-year period in which every male whose 18th year of life is completed (aged 18 and
in his 19th year of life) has to participate in military training and activities.13 The training
7Interestingly, the Prussian system of conscription, developed during the Napoleonic wars, became themodel for other European nations. France which abandoned conscription after Napoleon’s defeat in 1815,re-instituted it later with some restrictions. But, it reverted back to universal conscription after the defeatby the large conscripted German army, in the Franco-German War (1870-71). Russia, and later the SovietUnion, have had some form of conscription for most of the last two centuries. United States and Britainare two Western powers that tried to avoid conscription for most of the 19th and 20th centuries, except atthe time of the US Civil War, and the two World Wars (Britannica Academic, s.v. “Conscription,” accessedAugust 13, 2016, http://academic.eb.com.ezproxy.babson.edu/levels/collegiate/article/25932#.) One of theearly examples of conscription in developing countries is its introduction by Muhammad Ali (the OttomanAlbanian ruler of Egypt) in early 19th century in Egypt.
8For a thorough discussion of economics of conscription vs. all-volunteer force, see Warner and Asch(2001). They also offer empirical evidence that the arguments for all-volunteer force in the US are substan-tially stronger in 2001 than they were in 1973, when conscription was abolished.
9One may challenge military service on other dimensions as well. For instance, using draft lottery inArgentina, Galiani et al. (2011) show that conscription increased the chance of development of criminalrecord.
10There has been a complementarity between capital and labor in the military in all human history evenwith some modern technologies. For instance, guns need soldiers and tanks need drivers and gunners andvice versa. The advanced military technologies, such as smart drones, make capital more of a substitute forlabor rather than a complement. This reduces the need for labor in the armies. Moreover, some militarytechnologies that have considerable destructive capabilities cannot be confronted with armies of regularsoldiers. Therefore, return to regular soldiers has been declining over time, which has reduced the need forlarge standing armies.
11For a review of studies on recruitment, retention, military experience and productivity in the All-Volunteer Force (AVF) era in the United States, see Warner and Asch (1995).
12From 1906 to the Islamic Revolution of 1979, Iran had been a constitutional monarchy with a parliament.13The two-year training and service period has recently changed to 21 months.
6
lasted for three months and the other twenty one months were spent in service to the armed
forces (military and police). After that, there are three consecutive “reserve” periods: an
eight-year period, called “priority reserve” followed by two ten-year periods, “first reserve”
and “second reserve”. In these reserve periods, those who finished the two-year military
training and service are on reserve and would be called to service if needs arise (for example,
if the country goes to war). Priority would be given to those who are on the priority reserve,
and then the first and second reserves respectively.
According to law, those who are medically unfit for service (those with some disabilities
and chronic illnesses) are exempted. Tertiary level students are also exempted from military
service as long as they are in school. Students cannot stay in school more than a certain
number of years depending on the degree they pursue (Bachelor, Master, and Ph.D.). Upon
leaving the tertiary level institutions, they need to go to military service. Another form of
exemption, which is a basis of this paper, is given to the only sons whose fathers are 59 or
older14 at the time of conscription. The argument is that the elderly father may need his
sole son’s assistance in old age.15
2.1 Exemption and Education
This paper uses a combination of the sole son and student exemptions laws to identify a
discontinuity in educational attainment. Consider a male who is 18 years old and is the only
son but his father is 55 to 58 years old. He is just one to four years short of the threshold
needed for exemption by the law. But he can take advantage of the student exemption law
and go to college for four years so that he gets (temporary) student exemption in those years.
After he finishes his college (or even in the middle of it), his father is over 58 and he becomes
1459 was the threshold for father’s age until 2012. It has changed to 65 in 2012 and then to 70 in 2014.15There are other exemptions as well: 1) men who are the sole child of their parents, 2) men who are the
sole caregiver of a physically or mentally disabled parent, sibling, or second line family members, 3) doc-tors, firefighters, and other emergency workers whose military service may jeopardize health and emergencyservices, 4) employees of important governmental agencies that directly or indirectly assist the military areexempt at the time of war, 5) employees of businesses that work with the military are exempt from serviceat the time of war, 6) prisoners.
7
eligible for the sole son exemption. Compare him with someone who is 18 years old and is
the only son of a father who is 59 and older. This second man does not need to go to college
to get exemption. Therefore, those sole sons whose fathers’ age is below the threshold, when
they are 18, have more incentive to go to college or grad school and those whose fathers’
age is above have less. This may provide a discontinuity in educational attainment for the
sole sons whose fathers are younger and older than 58. Therefore, we can use regression
discontinuity method to estimate the effect of exemption law on educational attainment.
3 Data
In this study and for the reasons explained below, I use the 2% random and nationally
representative sample of the 2011 Census of Iran. Statistical Center of Iran (SCI) is the
main organization in charge of gathering micro datasets in Iran. It has been collecting the
national census every ten years starting in 1956 and every five years since 2006.16 The
latest one available to me is the 2011 census. The military exemption law for age 59 was
implemented between 2001 and 2011. The 2011 census includes individuals that were affected
throughout the whole period. This is one advantage of this dataset.
The 2011 census provides basic demographic data on age, education, labor market par-
ticipation, marital status, employment status, relationships within the household, as well as
the number of children ever born, and pregnancy in the 12 months prior to the survey. It also
contains information on the household’s dwelling and its amenities. It does not, however,
have information on the labor market outcome such as income and hours worked.
Education, which is the most important variable in this study, is reported with codes.
Unfortunately, more often than not, each code represents more than one grade. For example,
there is one code assigned to several grades of high school. Therefore, it is not possible to
find the years of education attained from these codes. But it is possible to identify the level
of education, i.e. primary, middle school, high school, and college and above. Hence, in this
16The censuses are 1956, 1966, 1976, 1986, 1996, 2006, and 2011.
8
study, only a dummy, that is equal to one when an individual has attained college or above
and zero otherwise, is defined and used as the main dependent variable. The number of years
of college or graduate school attended is not observed. Education code is not reported for
individuals who are illiterate. But whether an individual is literate or not is observed in the
census.17 When defining the college dummy variable, one should make sure that the dummy
for college education is zero for the illiterate even though the education code is missing for
them.
Age is another important variable in this study which is measured in years. I use the age
of the father when the sole son was 18 to identify whether or not the exemption law would
apply to the sole son. If the father is younger than 59 (i.e. 58 or younger), the law applies to
his sole son.18 In other words, the discontinuity in the exemption law starts at the end of age
58, just when the father becomes 59. In this study, sole sons are studied whose father’s age
was in a range around 5919 when they were 18. This is a very small sample of the population.
Therefore, one needs a dataset with many observations to be able to have enough number of
sole sons with fathers in that age range. The fact that the 2011 census has over 1.5 million
observations is another advantage of this dataset over other Iranian datasets.
One may argue that age is a variable for which “bunching” at certain values happens.
That is people are more likely to round their age to the closest multiple of 5. For example,
people aged 58, 59, or 61 are likely to report their age as 60. Therefore, usually in surveys,
the number of observations with ages that are multiples of 5 (like 40, 45, 50, etc.) is
larger than the number of observations with ages around those multiples. For example, the
number of observations with age 60 is larger than those with age 58, 59, or 61. But, here,
we are considering the age of father when the child was 18 not the age of father reported in
the survey. Therefore, bunching should not happen, particularly around the discontinuity
17A dummy variable is in the census that is one for the literate and zero otherwise.18Unless the son goes to college and postpone his illegibility for military service until his father becomes
older than 58 (59 or older).19The main age range used is 49 to 68 (±10 years around the threshold). Other age ranges used for
robustness check are 53 to 64 (±6), 51 to 66 (±8), 47 to 70 (±12), and 44 to 73 (±15).
9
threshold. Moreover, the distribution of father’s age when the child was 18 changes smoothly
in the sample and there is no evidence of bunching.
From the discussion of age, it is clear that we need to be able to identify fathers and
sole sons. This is not easy to achieve, since the demographic information in the data set
is very basic. None of the Iranian datasets identify a child’s father in the household. The
best variables that can be used to identify fathers (and other household members) are the
relationship to the head, and gender. The relationship to the head variable helps us identify,
the head’s spouse, the children of the head, the other relatives of the head who live with
the household including in-laws, and non-relatives. Using this variable, we can identify
households that only consist of a male head, his spouse, and his children. Obviously, male
heads in those households are fathers of individuals reported as their children. In the rare
case that the head of the household is reported as female and her spouse is male (0.2% of the
households), the spouse can be both the father or step-father. Therefore, those households
are not considered. In households that have extended family members, particularly multiple
generational households (households with grandfathers their children and grand children), it
is not possible to accurately identify fathers.
One issue with all Iranian datasets is that only the children present in the household are
recorded in the data and those who left the household for any reason (studying in another
place or marriage) are not in the data. This makes identifying the only sons difficult. For
example, a household that seems to have one son in the data, may actually have two or
more. The other son(s) left the household after marriage or to go to college in the city.
The 2011 census, however, asks for the number of children ever born by a mother (this is
another advantage of the 2011 census over other Iranian data sets.) It also asks who each
person’s mother is in the household. Using these two questions one can restrict the sample
to households whose children are all present. In order to do that, first, I restrict the sample
to households that only consist of a male head and his spouse and the children of the head.
In these households, only one mother is present. Then, we identify the number of children
10
who are present in the household and belong to the mother. We also know the number
of children the mother has ever born. Only households for which these two numbers are
the same are considered. These are the households that do not have children who left the
household. Therefore, it is possible to identify sole sons in them with accuracy. We focus on
these households. Table 1 shows the number of different types of households in the census
data including the final sample that is used.
It is unlikely that this algorithm identifies individuals who are not sole sons as sole sons.
But if that happens, this inaccurate identification of sole sons happens on both sides of
the threshold and hence does not bias the regression discontinuity results in favor of the
hypothesis. In other words, it does not create an effect when there is no effect. In fact,
measurement error in identification of sole sons creates a noisy estimate (larger standard
errors) and an under-estimation of the true effect of the military service law.
In rural areas, the size of the sample of sole sons (identified by the way explained above)
is quite small and inadequate to get statistical power. This is because of two reasons: 1)
we need to identify sole sons, aged 19 and over, from the sample of households all of whose
children are present. But, rural children (especially in this age range) are substantially
more likely not to be present in the household or have siblings who are not present in the
household, than their urban counterparts. They leave the household to migrate (temporarily
or permanently) to urban areas.They also marry earlier. 2) In 2011, less than 30% of the
population was living in rural areas. This means the size of the urban population was more
than twice the size of the rural population. For these two reasons, fewer sole sons can be
identified in rural areas.
Even if we have enough number of sole sons in rural areas to get statistical power, we still
cannot identify the true effect of exemption law on college attainment rates in rural areas.
This is because almost all college-educated (or college-enrolled) rural children went (or go)
to college in a city (not in the village) and usually stay there after graduation. In other
words, most college-educated or college student sole sons have already left the household
11
and are not part of the sample. As a result, we do not observe them to be able to measure
the impact of policies on the college attainment rates in rural areas. For all of these reasons,
we only focus on urban areas in this study.20
Another concern that one may have is measurement errors in father’s age. Since we are
interested in discontinuity close to a threshold of father’s age, measurement error in age close
to the threshold can easily take an observation from one side to the other side. This can
happen on both sides of the threshold but it only increases the standard error of the estimate
and produces an under-estimation of the true effect.
Table 2 reports the summary statistics of the variables in six samples: 1) Sole sons
(top-left panel), 2) Sisters of sole sons (top-right panel), 3) Sons in multiple-son households
(mid-left panel), 4) Daughters in multiple-son households (mid-right panel), 5) All men aged
19 to 28 (bottom-left panel), 6) All women aged 19 to 28 (bottom-right panel). Since the
military exemption law was applied between 2001 and 2010, in the 2011 census, the sole
sons are 19 to 28 years old. Therefore, all samples used in this study and reported in Table
2 are in the same age range. As can be seen the sample of sole sons and their sisters are
different from the general population (the bottom panel). They are slightly younger but
substantially more educated. Therefore, the results found in this study are Local Average
Treatment Effects (LATE) and may not be generalized to the population.
4 Estimations and Robustness Checks
As explained in Section 2.1, because of the exemption law we expect that sole sons whose
fathers were 58 or younger (when they were 18) attend college more than those whose fathers
were older than 58. This means that there should be a discontinuity in the college attainment
rate right after the father’s age of 58. This section documents this discontinuity and discusses
its consequences. Figure 1 documents the discontinuity in college attainment of the sole sons
20The results for rural areas are mostly statistically insignificant.
12
whose father’s age was between 49 and 68 when they were 18 years old, in urban areas21.
The figure has three sub-figures showing linear, quadratic, and polynomial fit of the data.
The horizontal axis shows the age of the father when his sole son was 18 and the vertical axis
is the share of sole sons who attended college and above. The solid lines are the fitted lines
and the light gray lines show the 95% confidence interval. The dots represent the average
college attendance rate at each father’s age. The discontinuous decline in college attendance
rate right after age 58, in all specifications in Figure 1, is evidence for the impact of the
exemption law.
We do not expect the exemption law to affect girls. So for robustness check, we can draw
a similar figure as Figure 1 for sisters of sole sons. Since sisters of sole sons are living in
the same household (have the same parents and face a somewhat similar environment), they
are a good comparison group. Figure 2 is drawn similar to Figure 1, but for these sisters.
The horizontal axis is the age of a father who has a sole son when his daughter (sole son’s
sister) was 18 and the vertical axis is the share of sole sons’ sisters who attended college and
above. The figure does not show any discontinuity in the college attainment rate for sisters
of sole sons right after the father’s age of 58. This provides further evidence that it was the
exemption law that affected college attainment of sole sons at father’s age of 58 or lower,
and not any other factor (particularly those that could have affected their sisters too).
To further strengthen the result we can also check to see if the exemption law had any
impact on sons in households with multiple sons. The law should not affect college attainment
rates of these sons. Figure 3 shows that this is the case. Although it may seem that there
was a decline, there is no statistically significant discontinuity in college attendance rate for
this group just after age 58. Similarly, one expects that the exemption law does not affect
daughters in households with multiple sons. Figure 4 is indeed the evidence for that. The
fact that only sole sons (and not the other three groups) have a discontinuity in college
attainment at the threshold is a strong evidence that military service exemption law caused
21As explained in Section 3, we only focus on urban areas because the sample of sole sons for rural areasis small and selected.
13
it.
We can formalize these figures in regressions as follows:
Yi = α + τDi +l∑
k=1
γk(pi − 58)k +l∑
k=1
δkDi(pi − 58)k + ui, l = 1, 2, 3 (1)
in which Yi is the college attendance for individual i. It is a dummy equal to one if the
individual attended (or is attending) college or higher levels of education and zero otherwise.
Di is a dummy equal to one if individual i’s father was 58 and younger when he/she was
18 and zero if the father’s age was 59 and higher.22 The running variable in this regression
discontinuity setting is father’s age when individual i was 18 and it is represented by pi.23
The regressions contain a polynomial of (pi− 58) with degree l.24 l is equal to one, two, and
three. The Local Average Treatment Effect (LATE) is τ .
Table 3 reports the results for Sole sons (left panel), sisters of sole sons (middle panel)
and sons in multiple-son households (right panel).25 The running variable, pi, in the sample
extends from 49 to 58 (i.e. ±10 years from the threshold). In other words, the samples
include individuals whose fathers were between the age of 49 and 68 when they were 18.26 pi
is a discrete rather than a continuous variable. Lee and Card (2008) and Lee and Lemieux
(2010) argue that when the running variable is discrete, correlation in standard errors should
be “clustered” at values of the discrete running variable. Therefore, all regressions correct
for robust heteroskedastic standard errors and correlation inside those clusters. The first
column of Table 3 explores college attendance for sole sons in a linear setting (l = 1 in
Equation (1)). The coefficient of D is positive and significant showing that sole sons whose
fathers’ age was 58 and slightly below when they were 18, have about 13 percentage points
22Other age ranges were analyzed as well and their results reported in Table 3. See footnote 19 .23p is chosen to denote this variable since it is the first letter of the Latin word pater and the Persian word
pedar , both meaning father.24The effect is the difference between actual and counterfactual at the threshold. Since the law affected
sole sons with father’s age of 58 not 59, we are interested in finding the counterfactual at the father’s age of58. This is why pi is subtracted from 58 in the regression.
25The results for daughters in multiple-son households are not reported because of limited space but areavailable upon the request.
26We change the range of the sample in Table 4 and get similar results.
14
more chance of attending college than those whose fathers’ age was 59 and slightly higher.
Since the average college attainment rate for the sample of sole sons is 66% (see Table 2),
the effect translates as a 20 percent increase.
Columns (2) and (3) report the quadratic and third-degree polynomial settings. The
coefficient of D remains statistically significant in those settings. It also increases in size
to 0.23 and 0.33 which translate into the LATE of 23 and 33 percentage points (35 and 50
percents respectively). For robustness check, one can run similar regressions for sole sons’
sisters. The results are reported in the middle panel of Table 3. All showing that there is no
evidence of discontinuity in college attendance for them (as the coefficient of D is statistically
insignificant). This indeed confirms that the discontinuity is something attributed to boys
and therefore related to the compulsory military service exemption. As another robustness
check, we can estimate the effect of military exemption law on sons living in households
with multiple sons. The right panel in Table 3, that is Columns (7), (8), and (9), report the
result of such estimation. The coefficient of D, which is the effect of the military exemption
law, is statistically insignificant, supporting the fact that this law should not affect college
attainment of sons in multiple-son households. For further robustness check, one can estimate
the same regressions for daughters living in multiple-son households. As expected and similar
to Figure 4, the effect of military service on this group is also statistically not different from
zero.27 All these results show that the discontinuity in college attainment rate only exists
for the sole sons, the only group that can benefit from the exemption law. Therefore, this
discontinuity at such a particular age of the father is evidence for the causal impact of the
law. Sole sons preferred to go to college rather than serve in the military and the exemption
law provided the incentive.
Since the discontinuity in college attainment for sole sons does not exit for their sisters
as well as sons and daughters of households with multiple sons, one can use the difference-
in-discontinuities design, as another robustness check to estimate the effect of the exemp-
27The results are not reported in Table 3, but are available upon request.
15
tion law. The difference-in-discontinuities (diff-in-disc) design, introduced by Grembi et al.
(2016), is a quasi-experimental method to identify effects of an intervention (especially if
the the observations on the two sides of threshold are inherently different).28 It estimates
the difference between two discontinuities in the outcomes, at the same threshold. By tak-
ing the difference between the two discontinuities, it removes any selection that may exist
around the threshold and is common between the two discontinuities. In our case, we can
practically take the difference between the discontinuity in college attainment for sole sons
and the discontinuity in college attainment for sisters of sole sons. This is essentially the
difference between Figures 1 and 2. Since there is no discontinuity for sisters of sole sons, we
can consider the difference between these two discontinuities as the effect of the exemption
law on sole sons. Instead of using the sisters of sole sons to compare with sole sons, one can
consider sons in multiple-son households or daughters in multiple-son households or a com-
bination of these groups. One advantage of this method is that it creates more power, since
we include sole sons and their sisters in the same regression. Moreover, if the observations
(in both the sole sons sample and the comparison group sample) before and after the father’s
age of 58 are inherently different from each other, under mild assumptions, the diff-in-disc
can remove this difference and give an unbiased result. It is hardly the case that there would
be an inherent difference between the observations on the two sides of the threshold (father’s
age of 58), but we can employ this method as a robustness check.
The difference-in-discontinuities design takes the shape of the following regression:
Yi = α + βDi +l∑
k=1
γk(pi − 58)k +l∑
k=1
δkDi(pi − 58)k +
Si{αs + τDi +l∑
k=1
γks(pi − 58)k +l∑
k=1
δksDi(pi − 58)k}+ ui, l = 1, 2, 3 (2)
in which Si is a dummy variable equal to one if individual i is a sole son and zero if she/he
28Grembi et al. (2016) call the estimated effect Neighborhood Average Treatment Effect (NATE).
16
belongs to a comparison group that was not affected by the exemption law. τ represents the
Neighborhood Average Treatment Effect (NATE), which in our case is the effect of the law
on sole sons’ college attainment rate at father’s age of 58.
We potentially can have three comparison groups: sisters of sole sons, sons in multiple-son
households and daughters in multiple-son households. We can run three separate diff-in-disc
using these groups, or combine these groups in various ways into a single comparison group.
Here, I estimate diff-in-disc between sole sons and their sisters first, and then gradually
add the sons and daughters in multiple-son households to the comparison group. Table 5
shows the estimated effects. The right panel reports τ using the sample of sole sisters as the
comparison group. This is referred to Sample I in the table. When we use this sample, Si in
Equation (2) is one for sole sons and zero for their sisters. Columns (1), (2), and (3) show
the estimates for this sample using linear, quadratic, and third-degree polynomial settings,
respectively. In Columns (4), (5), and (6), the sample of sons in multiple-son households
is added to the sample of sole sisters as the comparison group to form Sample II. In other
words, Si in Equation (2) is one for sole sons and zero for sisters of sole sons and sons
in multiple-son households. Finally, one can add the sample of daughters in multiple-son
households to the comparison group and form Sample III. In this sample, Si is zero for sisters
of sole sons as well as sons and daughters in multiple-son households. Columns (7), (8), and
(9) report the effect of the exemption law on sole sons using Sample III.
Interestingly, the size of the effects for the diff-in-disc design (particularly Samples II and
III) resembles the simple regression discontinuity estimates for sole sons reported in Table
3. The coefficients of D × S in linear, quadratic, and third-degree polynomials for Samples
I, II and III seem to be in the following ranges 0.11-0.13, 0.23-0.3, and 0.31-0.37 . These are
very close to the estimates reported in Columns (1), (2), and (3) of Table 3.29 This further
strengthens the results found previously in Table 3.
The results of this paper are strongly in line with anecdotal evidence. In fact, one reason
29In fact, except for the estimate in Column (2), all estimates in Table 5 are (statistically) identical to theones reported in the first three columns of Table 3.
17
that was officially mentioned in 2011 for raising the father’s age threshold for this law from 59
to 65 (and later to 70) was that non-eligible sole sons were going to college to get exemption
when their fathers reach 59.30
This discontinuity is an exogenous factor to college attainment and potentially can be
used as an instrument to estimate various returns to college education such as the return
on wages. The census does not provide data on earnings, wages, or hours worked.31 It
does, however, contain the job the individual has. One can use the job status or skill-
level required for the job as the labor market outcome variable. In the data, jobs are
divided into 9 categories: 1) legislators, senior officials and managers, 2) scientists, engineers,
lawyers and other professionals, 3) technicians and associate professionals, 4) office workers,
5) sellers, and semi-technical service workers, 6) semi-technical agricultural workers, 7) semi-
technical construction and industrial workers, 8) machine operators and drivers, 9) laborers
and unskilled workers. Based on this, I define a variable to represent the status of the job or
skills it requires and call it job status. It takes a larger value the more skill or status a job
has: five for the first job category (legislators, senior officials, and managers), four for the
second, three for the third job category, two for the fourth to eighth job categories, and one
for the last category. The results are robust to different definitions of the job status variable.
For example, one may define this variable to take nine values, each associated with a job
category. It can start at 1 for the ninth category and increase by one for each category until
it reaches 9 for legislators, senior officials and managers. The results are similar to what
follows.32
We can estimate the effect of college attainment on the job status variable using the
discontinuity as an instrument for college (and above) education. The number of sole sons
30Interview of Khabar-online with General Kamali, vice president of human resources for armedforces, on changes to military service conscription laws, (January 12, 2012), accessed October 17, 2015,http://khabaronline.ir/detail/193772/.
31Moreover, efforts in using other datasets, for which it was harder and problematic to identify sole sons,revealed that the discontinuity is a weak instrumental variable for college education. Therefore, any estimatesof returns to college education is biased. Results with the description of datasets used and the explanationof how sole sons were identified in those datasets are in the online appendix of the paper.
32The results can be found in the online appendix.
18
with jobs is small in which case the discontinuity is a weak instrument. To increase power,
we can combine sole sons and sons in multiple-son households in one sample and use the
diff-in-disc design in the first stage. In other words, Equation (2) becomes the first stage of
the two-stage least squares, in which Si is equal to one if individual i is a sole son and zero
if he is a son in a multiple-son household. In the second stage, the effect of college education
on job status is measured. I refrain from including sisters of sole sons and daughters in
multiple-son households in the comparison group, because the female labor supply decision
and the industries they work in are generally very different from men in Iran.
The first two columns of Table 6 reports the OLS and IV estimates of the effect of college
and above education on job status. The OLS results show that job status increases by one
level. The IV estimate, however, is 60% larger which may show that the actual effect of
education is larger than the OLS predicts. This could be a sign of measurement error in the
data. On the other hand, the Cragg-Donald Wald F-statistic for the first stage is small which
means the discontinuity is a weak instrument. As a result, unfortunately, the IV estimate
can be biased and unreliable and we may not be able to accurately estimate the effect of
college education on job status using this instrument.
The job status variable is more of a subjective measure than income. To give a sense
of how much the increase in job status due to college education means in monetary terms,
I introduce a new variable, AW, in the census data which is equal to the average wage in
each of these job status levels. These averages are calculated from the Iranian Household
Expenditure and Income Survey of 2010. So, all individuals who have the same job status,
get the same wage in the census. Running the same OLS and IV regressions and including
age and age squared, one finds the results reported in Columns (3) and (4) of Table 6. The
OLS estimate shows a 23.3% return on wages from college and above education while the
IV estimate depicts a 37.5% return.33 In other words, the return for every year of college
education is between 5 to 10 percent. Interestingly, these estimates are in the same range as
33The fact that the IV estimate is larger than the OLS estimate here is not surprising, given the fact thatthe IV estimate for job status was also larger than its OLS estimate.
19
the estimates of returns for the United States and European countries. But one should note
that these are the returns for a young sample (19 to 28 year-olds). As one ages, returns may
become larger because of complementarities between education and experience.34 In both
OLS and IV regressions, the coefficients of age profile variables, age and age squared, are
insignificant. This could be because the age range is small and individuals are early in their
career.
One may wonder how much the estimated returns in Columns (3) and (4) are different
from the OLS estimates of the return for the general population, when one uses individual
level wages, W, rather than aggregate averages, AW. Using Household Expenditure and
Income Survey (HEIS) of 2010, Column (5) reports the return to college and above education
for wages. The dependent variable is the natural log of wages, ln(W). The return is estimated
to be 28%. An individual with college and above education on average earns 28% more wages.
This estimate is similar to the estimate in Column (4) for the smaller sample of sole sons and
sons in multiple-son households (who still live with their parents.) Column (6) reports the
same OLS regression but the dependent variable is the average wage of the job status level
of the individual, AW. The estimated return is 25.5% which is even closer to the estimated
return in Column (4). This means that the average return for the specific sample we used
in this study is not different from the general population.
Unfortunately, it is not possible to use the Household Expenditure and Income Surveys
to estimate the return to college education using the discontinuity. This is because these
surveys do not contain the number of children ever born and as non-present members of the
household are not recorded in the data, it is not possible to identify households with sole
sons in these datasets.
34These complementarities cannot be captured by the age profile variables.
20
5 Conclusion
This study, for the first time, documents that a discontinuity in the law for military service
exemption has created a wedge in education levels of sole sons. Sole sons whose fathers were
58 or slightly younger, when they were 18, were 20% more likely to go to college only to
get exemption from the service. In other words, if the military exemption law did not exist,
they would have gone to college less. Military service is not favored by sole sons and they
chose against their will to go to college to avoid it.
This is while entering college especially until the late 2000s has been very competitive
and challenging as the supply of college seats has been a fraction of demand. It required the
individual to be ranked in the top 30% in the national college entrance examination, known
as Konkoor 35. It was estimated that in 2011 alone, households spent about 4.3 billion USD36
on supplemental educational sources beyond school, such as supplemental test taking books
and tutoring, to help their children get an edge in Konkoor.37 Interestingly, this amounts to
nearly half of the Ministry of Education’s budget for that year. According to the census data,
2.52% of households have someone who is planning to go to college. This amounts to about
520,000 households in 2011. Assuming that all of these households spent on supplemental
educational sources outside of school, every household spent about 8,000 USD in 2011 which
was roughly about 1.15 times the GDP per capita of Iran in that year. In other words, an
average urban household is willing to spend more than the income of one year to increase the
chance of their child going to college. Anecdotal evidence supports the fact that households
start saving early to be able to afford such expenses, especially that college education can
be free if the students gets into one of the public universities (which have a higher quality
as well.)
35It is based on the French word concours meaning contest.3670,000 billion Iranian Rials.37Source of data is Ali Abbaspour Tehrani, vice-chairman of the parliamentary committee on education
and research, who mentioned it in an interview with Aseman Weekly (A Persian periodical in Iran), inDecember 2012. The Weekly is out of print but this part of the interview is reported on other websites,particularly, The Iranian Student News Agency (ISNA) on December 25, 2012, accessed August 23, 2016http://www.isna.ir/news/91100503204/.
21
In this market for higher education, sole sons face an intense competition and a chal-
lenging task to enter college. But they are willing to go through this ordeal and come out
successfully just to avoid conscription (even) in peace times. Using this exogenous increase
in education, there is some evidence that this quest for education is beneficial to sole sons
in the labor market, but the problem of weak instrument makes the results unreliable. If
we had a larger sample of sole sons, the discontinuity could be a strong instrument for col-
lege attainment. More information on various outcomes of the individuals such as cognitive
and non-cognitive skills, health, or basic labor market outcomes such as wages and hours
worked, could help us identify the many impacts of college education and conscription in
Iran. Further research may open horizons to do so.
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24
Figure 1: Discontinuity in College Attendance of Sole Sons
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Sole Son's Father
Linear fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Sole Son's Father
Quadratic fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Sole Son's Father
Polynomial fit
Figure 2: Discontinuity in College Attendance of Sole Sons’ Sisters
0.5
11.
5C
olle
ge a
ttend
ance
50 55 60 65 70Age of the Sisters' Father
Linear fit
0.5
11.
5C
olle
ge a
ttend
ance
50 55 60 65 70Age of the Sisters' Father
Quadratic fit
0.5
11.
5C
olle
ge a
ttend
ance
50 55 60 65 70Age of the Sisters' Father
Polynomial fit
25
Figure 3: Discontinuity in College Attendance of Sons in Multiple Son Households
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Son's Father
Linear fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Son's Father
Quadratic fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Son's Father
Polynomial fit
Figure 4: Discontinuity in College Attendance of Daughters in Multiple Son Households
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Daughter's Father
Linear fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce
50 55 60 65 70Age of the Daughter's Father
Quadratic fit
0.2
.4.6
.81
Col
lege
atte
ndan
ce50 55 60 65 70
Age of the Daughter's Father
Polynomial fit
26
Table 1: Types and Number of Households in the 2011 Census
Share out of allNumber Households with
head (in %)
All Households 423,637
without head 1,303
with head 422,334 100.0
but no spouse 68,341 16.2
and one spouse 353,001 83.6
and more than one spouse 992 0.2
Households with head living with
sons or daughter-in-laws of the head 6,312 1.5
parents of head or spouse 10,329 2.5
siblings of head or spouse 5,904 1.4
other relatives and non-relatives 2,542 0.6
Households with a mother and her husband 274,382 65.0
with all children present* 129,619 30.7
Sample of this study† 1,431 0.3
Note: This table contains the number of households in the census based on the
composition of the household. There are 1,481,586 individuals in the data.
Only 0.4 percent of them (5,840) are living in households without head. This study
only focuses on households that have a head and one spouse but do not have
any other member except children. These households may not have children at all.
* The households in this sample are identified using two rules: 1) The households
only consist of a male head, his spouse, and their children (no step-child,
another spouse, or any extended family member), 2) the number of father’s
children present in the household is equal to the number of children ever born by
the mother. Table 2 contains summary statistics of these mothers.† This is the sample of sole sons whose fathers age was between 49 and
68 when they were 18.
27
Table 2: Summary Statistics
Sole Sons Sisters of Sole Sons
N Mean St. Dev. Min. Max. N Mean St. Dev. Min. Max.
Age 1,431 22.4 2.6 19 28 1,771 22.1 2.6 19 28
Primary or Middle School 1,431 0.06 0.23 0 1 1,771 0.03 0.16 0 1
High School 1,431 0.27 0.44 0 1 1,771 0.18 0.39 0 1
College & Above 1,431 0.66 0.48 0 1 1,771 0.78 0.42 0 1
Sons in Multiple-son HHs Daughters in Multiple-son HHs
Age 3,065 22.2 2.6 19 28 1,009 22.2 2.6 19 28
Primary or Middle School 3,065 0.11 0.31 0 1 1,009 0.08 0.27 0 1
High School 3,065 0.33 0.47 0 1 1,009 0.26 0.44 0 1
College & Above 3,065 0.53 0.50 0 1 1,009 0.63 0.48 0 1
Men Aged 19-28 Women Aged 19-28
Age 114,303 23.8 2.8 19 28 119,331 23.8 2.8 19 28
Primary or Middle School 114,303 0.27 0.44 0 1 119,331 0.20 0.40 0 1
High School 114,303 0.36 0.48 0 1 119,331 0.37 0.48 0 1
College & Above 114,303 0.34 0.47 0 1 119,331 0.40 0.49 0 1
Note: Age is the age of the individual at the time of survey. Urban is a dummy equal to one if the individual lives in an urban area
and zero otherwise. Primary or Middle School is a dummy equal to one if the individual’s last level of education was primary or
middle school and zero otherwise. High school is a dummy equal to one if the individual’s last level of education was high school.
College attendance is a dummy variable equal to one if the individual attended college or higher levels of education and zero
otherwise. The means of “Primary or Middle School”,“High School”, and “College & Above” do not add up to one, since around 1 to
3 percent of the samples are illiterate. All samples include ages 19 to 28 only.
28
Table 3: Discontinuity in College Education
Sole Sons Sole Sons’ Sisters Sons in Multiple-Son HH
(1) (2) (3) (4) (5) (6) (7) (8) (9)
D 0.127** 0.228*** 0.329*** -0.009 -0.075 -0.044 0.044 0.064 0.080(0.054) (0.052) (0.114) (0.049) (0.071) (0.072) (0.033) (0.039) (0.079)
p− 58 -0.000 0.077** 0.124 0.002 -0.063** -0.083 -0.014*** -0.011 0.030(0.009) (0.031) (0.109) (0.009) (0.028) (0.055) (0.004) (0.027) (0.084)
(p− 58)2 × 10−1 0.005 -0.089** -0.065 -0.003 0.082** 0.169** 0.016*** 0.020 -0.056(0.011) (0.036) (0.117) (0.011) (0.037) (0.066) (0.005) (0.028) (0.086)
(p− 58)3 × 10−4 -0.080** -0.191 0.069* 0.116 -0.004 -0.101(0.034) (0.244) (0.034) (0.140) (0.028) (0.175)
D(p− 58) 0.064 0.355 -0.050 0.076 0.011 0.018(0.038) (0.263) (0.040) (0.162) (0.030) (0.182)
D(p− 58)2 × 10−1 7.119 -3.062 6.328(14.943) (10.241) (10.122)
D(p− 58)3 × 10−4 5.154 14.828 -12.510(16.194) (11.515) (10.695)
Constant 0.570*** 0.438*** 0.391*** 0.777*** 0.879*** 0.898*** 0.512*** 0.506*** 0.464***(0.044) (0.040) (0.097) (0.031) (0.035) (0.051) (0.030) (0.030) (0.078)
Observations 1,431 1,431 1,431 1,771 1,771 1,771 3,065 3,065 3,065
Note: Sample used in regression in the left panel only includes sole sons whose father’s age when they were 18 was between 49 and 68. Sample
of the regressions in the middle panel includes sisters of sole sons whose father’s age when the sister was 18 was between 49 and 68. Sample
used in the right panel includes sons in multiple son households whose father’s age when the son was 18 was between 49 and 68. Dependent
variable is college attendance explained in notes for Table 2. D is a dummy equal to 1 if the father’s age of a sole son is less than 59 when the
son was 18 years old and zero otherwise. p is father’s age. Robust-heteroskedastic standard errors corrected for correlation inside
clusters are in parentheses. Clusters are values of father’s age when son was 18.
* p<0.10, ** p<0.05, *** p<0.01
29
Table 4: Discontinuity in College Education Across Various Samples (τ in Equation (1))
Sole Sons Sole Sons’ Sisters Sons in Multiple-Son HHs
(1) (2) (3) (4) (5) (6) (7) (8) (9)
53 to 64 (±6) 0.229*** 0.250** -0.109 -0.058 0.039 0.047 0.065* 0.051 0.089(0.055) (0.088) (0.185) (0.064) (0.048) (0.089) (0.030) (0.077) (0.156)
Obs. 580 580 580 701 701 701 1,160 1,160 1,160
51 to 66 (±8) 0.132** 0.321*** 0.111 -0.037 -0.052 0.065 0.047 0.067 0.071(0.052) (0.072) (0.121) (0.055) (0.066) (0.060) (0.030) (0.051) (0.097)
Obs. 939 939 939 1,117 1,117 1,117 1,965 1,965 1,965
47 to 70 (±12) 0.127** 0.181*** 0.353*** 0.011 -0.044 -0.140 0.040 0.056 0.073(0.052) (0.057) (0.092) (0.045) (0.078) (0.092) (0.032) (0.034) (0.064)
Obs. 2,145 2,145 2,145 2,645 2,645 2,645 4,673 4,673 4,673
44 to 73 (±15) 0.114* 0.187*** 0.279*** -0.011 0.014 -0.137 0.073* 0.008 0.101*(0.059) (0.051) (0.071) (0.037) (0.069) (0.086) (0.039) (0.033) (0.057)
Obs. 3,501 3,501 3,501 4,309 4,309 4,309 7,861 7,861 7,861
Polynomial order 1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd
Note: See notes for Table 3 for information on each panel in the table. Only the coefficient of D in Equation (1) is reported for
various samples. Father’s age between 53 and 64 (±6 years around the threshold, Father’s age between 51 and 66 (±8 years around
the threshold), father’s age between 47 to 70 (±12 years around the threshold), father’s age between 44 and 73 (±15 years around
the threshold). when they were 18 was between 50 and 68. Robust-heteroskedastic standard errors corrected for correlation inside
clusters are in parentheses. Clusters are values of father’s age when son was 18.
* p<0.10, ** p<0.05, *** p<0.01
30
Table 5: Discontinuity in College Attendance of Sole Sons (τ in Equation (2))
Sample I Sample II Sample III
(1) (2) (3) (4) (5) (6) (7) (8) (9)
D × S 0.136* 0.303*** 0.372** 0.118* 0.232*** 0.341** 0.107* 0.225*** 0.307**(0.076) (0.091) (0.147) (0.059) (0.056) (0.130) (0.058) (0.056) (0.122)
Polynomial order 1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd
Observations 3,202 3,202 3,202 6,267 6,267 6,267 7,276 7,276 7,276
Note: Regressions are based on Equation (2). See notes for Table 3 for more information on variables. Coefficient of D × S shows
the Neighborhood Average Treatment Effect from a Diff-in-Disc regression. Sample I includes sole sons and sisters of sole sons.
Sample II adds sons in multiple-son households to Sample I. Sample III adds daughters in multiple-son households to Sample II.
Robust-heteroskedastic corrected for correlation inside clusters are in parentheses. Clusters are father’s age of the individual when
s/he was 18.
* p<0.10, ** p<0.05, *** p<0.01
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Table 6: OLS and IV Estimates of Return to College Education on Job Status and Wages
Job Status ln(AW) OLS HEIS†
OLS IV OLS IV ln(W) ln(AW)(1) (2) (3) (4) (5) (6)
College & above 1.009*** 1.633*** 0.233*** 0.375*** 0.281*** 0.252***(0.088) (0.315) (0.021) (0.084) (0.038) (0.010)
Age -0.039 -0.041 0.141 -0.015(0.044) (0.048) (0.107) (0.016)
Age2 × 10−2 0.096 0.080 -0.001 0.000(0.096) (0.103) (0.002) (0.000)
Constant 1.954*** 1.802*** 13.571*** 13.663*** 10.480*** 13.272***(0.024) (0.083) (0.494) (0.570) (1.287) (0.191)
F-statistic† 2.658 2.466Observations 774 774 774 774 2,978 3,387Adj. R-squared 0.227 0.140 0.242 0.177 0.100 0.358
Note: Job Status is a variable that is one if the individual is an unskilled worker, two for semi-technical
workers, three for technicians and associate professionals, four for scientists, doctors, lawyers, and engineers,
and five for managers, and top government officials. ln(AW) is the natural log of the average wage for the
job status an individual has. ln(W) is the natural log of individual’s wage. IV estimates are 2SLS estimates
with Equation (2) as the first stage and the discontinuity as an instrument for college education. Robust-
heteroskedastic standard errors in parentheses.† OLS estimates using HEIS 2010 is the Household Expenditure and Income Survey of 2010.
* p<0.10, ** p<0.05, *** p<0.01
32
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